Overview

Dataset statistics

Number of variables25
Number of observations8343
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory200.0 B

Variable types

Numeric12
Categorical13

Warnings

f2f_total_12 has constant value "0" Constant
webinar_total_12 has constant value "0" Constant
f2f_total_03 is highly correlated with f2f_total_06 and 1 other fieldsHigh correlation
f2f_total_06 is highly correlated with f2f_total_03 and 1 other fieldsHigh correlation
f2f_total_09 is highly correlated with f2f_total_03 and 1 other fieldsHigh correlation
is_cardiologist is highly correlated with is_gpHigh correlation
is_gp is highly correlated with is_cardiologistHigh correlation
gender_female is highly correlated with gender_maleHigh correlation
gender_male is highly correlated with gender_femaleHigh correlation
hospital is highly correlated with officeHigh correlation
office is highly correlated with hospitalHigh correlation
f2f_total_03 is highly correlated with f2f_total_06 and 1 other fieldsHigh correlation
f2f_total_06 is highly correlated with f2f_total_03 and 1 other fieldsHigh correlation
f2f_total_09 is highly correlated with f2f_total_03 and 1 other fieldsHigh correlation
is_cardiologist is highly correlated with is_gpHigh correlation
is_gp is highly correlated with is_cardiologistHigh correlation
gender_female is highly correlated with gender_maleHigh correlation
gender_male is highly correlated with gender_femaleHigh correlation
hospital is highly correlated with officeHigh correlation
office is highly correlated with hospitalHigh correlation
f2f_total_06 is highly correlated with f2f_total_09High correlation
f2f_total_09 is highly correlated with f2f_total_06High correlation
is_cardiologist is highly correlated with is_gpHigh correlation
is_gp is highly correlated with is_cardiologistHigh correlation
gender_female is highly correlated with gender_maleHigh correlation
gender_male is highly correlated with gender_femaleHigh correlation
hospital is highly correlated with officeHigh correlation
office is highly correlated with hospitalHigh correlation
hospital is highly correlated with office and 2 other fieldsHigh correlation
gender_male is highly correlated with gender_femaleHigh correlation
rep_id is highly correlated with is_cardiologist and 1 other fieldsHigh correlation
office is highly correlated with hospital and 2 other fieldsHigh correlation
f2f_total_03 is highly correlated with is_cardiologist and 3 other fieldsHigh correlation
is_cardiologist is highly correlated with hospital and 4 other fieldsHigh correlation
f2f_total_06 is highly correlated with f2f_total_03 and 1 other fieldsHigh correlation
gender_female is highly correlated with gender_maleHigh correlation
webinar_total_03 is highly correlated with webinar_total_06High correlation
webinar_total_06 is highly correlated with webinar_total_03High correlation
is_gp is highly correlated with hospital and 4 other fieldsHigh correlation
f2f_total_09 is highly correlated with f2f_total_03 and 1 other fieldsHigh correlation
conference_total_03 is highly correlated with webinar_total_12 and 1 other fieldsHigh correlation
is_cardiologist is highly correlated with is_gp and 2 other fieldsHigh correlation
office is highly correlated with webinar_total_12 and 2 other fieldsHigh correlation
is_gp is highly correlated with is_cardiologist and 2 other fieldsHigh correlation
gender_female is highly correlated with webinar_total_12 and 2 other fieldsHigh correlation
webinar_total_12 is highly correlated with conference_total_03 and 11 other fieldsHigh correlation
conference_total_09 is highly correlated with webinar_total_12 and 1 other fieldsHigh correlation
hospital is highly correlated with office and 2 other fieldsHigh correlation
conference_total_06 is highly correlated with webinar_total_12 and 1 other fieldsHigh correlation
gender_male is highly correlated with gender_female and 2 other fieldsHigh correlation
conference_total_12 is highly correlated with webinar_total_12 and 1 other fieldsHigh correlation
webinar_total_09 is highly correlated with webinar_total_12 and 1 other fieldsHigh correlation
f2f_total_12 is highly correlated with conference_total_03 and 11 other fieldsHigh correlation
email_open_total_03 has 3095 (37.1%) zeros Zeros
email_open_total_06 has 3026 (36.3%) zeros Zeros
email_open_total_09 has 3124 (37.4%) zeros Zeros
email_open_total_12 has 3041 (36.4%) zeros Zeros
f2f_total_03 has 2152 (25.8%) zeros Zeros
f2f_total_06 has 2147 (25.7%) zeros Zeros
f2f_total_09 has 2486 (29.8%) zeros Zeros
webinar_total_03 has 7586 (90.9%) zeros Zeros
webinar_total_06 has 7596 (91.0%) zeros Zeros
prescription_total has 1527 (18.3%) zeros Zeros

Reproduction

Analysis started2021-06-01 14:56:51.758083
Analysis finished2021-06-01 14:57:10.023034
Duration18.26 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

rep_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct153
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154.2720844
Minimum100
Maximum252
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:10.144560image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile102
Q1124
median149
Q3185
95-th percentile218
Maximum252
Range152
Interquartile range (IQR)61

Descriptive statistics

Standard deviation36.61065761
Coefficient of variation (CV)0.2373122639
Kurtosis-0.8445532806
Mean154.2720844
Median Absolute Deviation (MAD)29
Skewness0.3773892915
Sum1287092
Variance1340.34025
MonotonicityNot monotonic
2021-06-01T16:57:10.262357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102360
 
4.3%
145201
 
2.4%
191175
 
2.1%
147175
 
2.1%
125172
 
2.1%
129143
 
1.7%
151135
 
1.6%
124132
 
1.6%
153126
 
1.5%
105126
 
1.5%
Other values (143)6598
79.1%
ValueCountFrequency (%)
10091
 
1.1%
10179
 
0.9%
102360
4.3%
10333
 
0.4%
10431
 
0.4%
105126
 
1.5%
106123
 
1.5%
107119
 
1.4%
10855
 
0.7%
10929
 
0.3%
ValueCountFrequency (%)
2521
 
< 0.1%
2511
 
< 0.1%
2501
 
< 0.1%
2491
 
< 0.1%
2481
 
< 0.1%
2473
 
< 0.1%
2461
 
< 0.1%
2451
 
< 0.1%
24414
0.2%
2431
 
< 0.1%

conference_total_03
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
7568 
1
 
737
2
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07568
90.7%
1737
 
8.8%
238
 
0.5%

Length

2021-06-01T16:57:10.480801image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:10.543778image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
07568
90.7%
1737
 
8.8%
238
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07568
90.7%
1737
 
8.8%
238
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07568
90.7%
1737
 
8.8%
238
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07568
90.7%
1737
 
8.8%
238
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07568
90.7%
1737
 
8.8%
238
 
0.5%

conference_total_06
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
7521 
1
779 
2
 
43

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07521
90.1%
1779
 
9.3%
243
 
0.5%

Length

2021-06-01T16:57:10.719046image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:10.782936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
07521
90.1%
1779
 
9.3%
243
 
0.5%

Most occurring characters

ValueCountFrequency (%)
07521
90.1%
1779
 
9.3%
243
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07521
90.1%
1779
 
9.3%
243
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07521
90.1%
1779
 
9.3%
243
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07521
90.1%
1779
 
9.3%
243
 
0.5%

conference_total_09
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
7551 
1
763 
2
 
27
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07551
90.5%
1763
 
9.1%
227
 
0.3%
32
 
< 0.1%

Length

2021-06-01T16:57:10.954253image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:11.019575image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
07551
90.5%
1763
 
9.1%
227
 
0.3%
32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07551
90.5%
1763
 
9.1%
227
 
0.3%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07551
90.5%
1763
 
9.1%
227
 
0.3%
32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07551
90.5%
1763
 
9.1%
227
 
0.3%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07551
90.5%
1763
 
9.1%
227
 
0.3%
32
 
< 0.1%

conference_total_12
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
7533 
1
779 
2
 
30
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
07533
90.3%
1779
 
9.3%
230
 
0.4%
31
 
< 0.1%

Length

2021-06-01T16:57:11.192612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:11.257672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
07533
90.3%
1779
 
9.3%
230
 
0.4%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07533
90.3%
1779
 
9.3%
230
 
0.4%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07533
90.3%
1779
 
9.3%
230
 
0.4%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07533
90.3%
1779
 
9.3%
230
 
0.4%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07533
90.3%
1779
 
9.3%
230
 
0.4%
31
 
< 0.1%

email_open_total_03
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9858564066
Minimum0
Maximum7
Zeros3095
Zeros (%)37.1%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:11.316159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9911095483
Coefficient of variation (CV)1.005328506
Kurtosis1.031497074
Mean0.9858564066
Median Absolute Deviation (MAD)1
Skewness1.014062707
Sum8225
Variance0.9822981367
MonotonicityNot monotonic
2021-06-01T16:57:11.400095image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
13107
37.2%
03095
37.1%
21490
17.9%
3503
 
6.0%
4114
 
1.4%
532
 
0.4%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
03095
37.1%
13107
37.2%
21490
17.9%
3503
 
6.0%
4114
 
1.4%
532
 
0.4%
61
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
61
 
< 0.1%
532
 
0.4%
4114
 
1.4%
3503
 
6.0%
21490
17.9%
13107
37.2%
03095
37.1%

email_open_total_06
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.022414
Minimum0
Maximum6
Zeros3026
Zeros (%)36.3%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:11.481284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.01137196
Coefficient of variation (CV)0.9892000308
Kurtosis0.6611379464
Mean1.022414
Median Absolute Deviation (MAD)1
Skewness0.9391927051
Sum8530
Variance1.022873242
MonotonicityNot monotonic
2021-06-01T16:57:11.561299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
03026
36.3%
13019
36.2%
21586
19.0%
3539
 
6.5%
4144
 
1.7%
528
 
0.3%
61
 
< 0.1%
ValueCountFrequency (%)
03026
36.3%
13019
36.2%
21586
19.0%
3539
 
6.5%
4144
 
1.7%
528
 
0.3%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
528
 
0.3%
4144
 
1.7%
3539
 
6.5%
21586
19.0%
13019
36.2%
03026
36.3%

email_open_total_09
Real number (ℝ≥0)

ZEROS

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9880139039
Minimum0
Maximum7
Zeros3124
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:11.641464image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum7
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9972872213
Coefficient of variation (CV)1.009385817
Kurtosis0.9381677242
Mean0.9880139039
Median Absolute Deviation (MAD)1
Skewness0.9980709947
Sum8243
Variance0.9945818018
MonotonicityNot monotonic
2021-06-01T16:57:11.726201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
03124
37.4%
13046
36.5%
21509
18.1%
3508
 
6.1%
4130
 
1.6%
522
 
0.3%
63
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
03124
37.4%
13046
36.5%
21509
18.1%
3508
 
6.1%
4130
 
1.6%
522
 
0.3%
63
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
63
 
< 0.1%
522
 
0.3%
4130
 
1.6%
3508
 
6.1%
21509
18.1%
13046
36.5%
03124
37.4%

email_open_total_12
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.005633465
Minimum0
Maximum6
Zeros3041
Zeros (%)36.4%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:11.809043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9990846462
Coefficient of variation (CV)0.9934878669
Kurtosis0.8401131661
Mean1.005633465
Median Absolute Deviation (MAD)1
Skewness0.9727254529
Sum8390
Variance0.9981701303
MonotonicityNot monotonic
2021-06-01T16:57:11.889479image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
13087
37.0%
03041
36.4%
21533
18.4%
3522
 
6.3%
4133
 
1.6%
523
 
0.3%
64
 
< 0.1%
ValueCountFrequency (%)
03041
36.4%
13087
37.0%
21533
18.4%
3522
 
6.3%
4133
 
1.6%
523
 
0.3%
64
 
< 0.1%
ValueCountFrequency (%)
64
 
< 0.1%
523
 
0.3%
4133
 
1.6%
3522
 
6.3%
21533
18.4%
13087
37.0%
03041
36.4%

f2f_total_03
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.171760758
Minimum0
Maximum21
Zeros2152
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:11.978643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum21
Range21
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.196498675
Coefficient of variation (CV)1.011390719
Kurtosis5.622076836
Mean2.171760758
Median Absolute Deviation (MAD)1
Skewness1.73924521
Sum18119
Variance4.824606429
MonotonicityNot monotonic
2021-06-01T16:57:12.078202image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
02152
25.8%
11675
20.1%
21551
18.6%
31128
13.5%
4771
 
9.2%
5475
 
5.7%
6242
 
2.9%
7144
 
1.7%
881
 
1.0%
936
 
0.4%
Other values (10)88
 
1.1%
ValueCountFrequency (%)
02152
25.8%
11675
20.1%
21551
18.6%
31128
13.5%
4771
 
9.2%
5475
 
5.7%
6242
 
2.9%
7144
 
1.7%
881
 
1.0%
936
 
0.4%
ValueCountFrequency (%)
211
 
< 0.1%
185
 
0.1%
172
 
< 0.1%
162
 
< 0.1%
154
 
< 0.1%
147
 
0.1%
137
 
0.1%
1210
 
0.1%
1121
0.3%
1029
0.3%

f2f_total_06
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.221503056
Minimum0
Maximum19
Zeros2147
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:12.169826image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.278464489
Coefficient of variation (CV)1.025640942
Kurtosis4.834519456
Mean2.221503056
Median Absolute Deviation (MAD)1
Skewness1.717823703
Sum18534
Variance5.191400426
MonotonicityNot monotonic
2021-06-01T16:57:12.263472image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
02147
25.7%
11731
20.7%
21379
16.5%
31194
14.3%
4771
 
9.2%
5496
 
5.9%
6232
 
2.8%
7138
 
1.7%
888
 
1.1%
957
 
0.7%
Other values (10)110
 
1.3%
ValueCountFrequency (%)
02147
25.7%
11731
20.7%
21379
16.5%
31194
14.3%
4771
 
9.2%
5496
 
5.9%
6232
 
2.8%
7138
 
1.7%
888
 
1.1%
957
 
0.7%
ValueCountFrequency (%)
191
 
< 0.1%
181
 
< 0.1%
171
 
< 0.1%
166
 
0.1%
158
 
0.1%
144
 
< 0.1%
1314
0.2%
1221
0.3%
1124
0.3%
1030
0.4%

f2f_total_09
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.924247872
Minimum0
Maximum17
Zeros2486
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:12.357629image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum17
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.021999737
Coefficient of variation (CV)1.050800038
Kurtosis4.635370616
Mean1.924247872
Median Absolute Deviation (MAD)1
Skewness1.638157258
Sum16054
Variance4.088482936
MonotonicityNot monotonic
2021-06-01T16:57:12.450305image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
02486
29.8%
11750
21.0%
21486
17.8%
31081
13.0%
4702
 
8.4%
5386
 
4.6%
6202
 
2.4%
7106
 
1.3%
857
 
0.7%
934
 
0.4%
Other values (8)53
 
0.6%
ValueCountFrequency (%)
02486
29.8%
11750
21.0%
21486
17.8%
31081
13.0%
4702
 
8.4%
5386
 
4.6%
6202
 
2.4%
7106
 
1.3%
857
 
0.7%
934
 
0.4%
ValueCountFrequency (%)
172
 
< 0.1%
162
 
< 0.1%
153
 
< 0.1%
144
 
< 0.1%
1310
 
0.1%
124
 
< 0.1%
1110
 
0.1%
1018
 
0.2%
934
0.4%
857
0.7%

f2f_total_12
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
8343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08343
100.0%

Length

2021-06-01T16:57:12.646083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:12.712834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
08343
100.0%

Most occurring characters

ValueCountFrequency (%)
08343
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08343
100.0%

webinar_total_03
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1147069399
Minimum0
Maximum6
Zeros7586
Zeros (%)90.9%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:12.768896image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.417092472
Coefficient of variation (CV)3.636157256
Kurtosis41.55089498
Mean0.1147069399
Median Absolute Deviation (MAD)0
Skewness5.306139986
Sum957
Variance0.1739661302
MonotonicityNot monotonic
2021-06-01T16:57:12.855010image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
07586
90.9%
1621
 
7.4%
295
 
1.1%
329
 
0.3%
45
 
0.1%
64
 
< 0.1%
53
 
< 0.1%
ValueCountFrequency (%)
07586
90.9%
1621
 
7.4%
295
 
1.1%
329
 
0.3%
45
 
0.1%
53
 
< 0.1%
64
 
< 0.1%
ValueCountFrequency (%)
64
 
< 0.1%
53
 
< 0.1%
45
 
0.1%
329
 
0.3%
295
 
1.1%
1621
 
7.4%
07586
90.9%

webinar_total_06
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1323265013
Minimum0
Maximum9
Zeros7596
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:12.939910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5074264521
Coefficient of variation (CV)3.834654791
Kurtosis51.15379817
Mean0.1323265013
Median Absolute Deviation (MAD)0
Skewness5.889979497
Sum1104
Variance0.2574816042
MonotonicityNot monotonic
2021-06-01T16:57:13.025548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
07596
91.0%
1522
 
6.3%
2148
 
1.8%
347
 
0.6%
420
 
0.2%
75
 
0.1%
53
 
< 0.1%
91
 
< 0.1%
61
 
< 0.1%
ValueCountFrequency (%)
07596
91.0%
1522
 
6.3%
2148
 
1.8%
347
 
0.6%
420
 
0.2%
53
 
< 0.1%
61
 
< 0.1%
75
 
0.1%
91
 
< 0.1%
ValueCountFrequency (%)
91
 
< 0.1%
75
 
0.1%
61
 
< 0.1%
53
 
< 0.1%
420
 
0.2%
347
 
0.6%
2148
 
1.8%
1522
 
6.3%
07596
91.0%

webinar_total_09
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
7956 
1
 
354
2
 
32
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07956
95.4%
1354
 
4.2%
232
 
0.4%
31
 
< 0.1%

Length

2021-06-01T16:57:13.258462image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:13.325953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
07956
95.4%
1354
 
4.2%
232
 
0.4%
31
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
07956
95.4%
1354
 
4.2%
232
 
0.4%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07956
95.4%
1354
 
4.2%
232
 
0.4%
31
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07956
95.4%
1354
 
4.2%
232
 
0.4%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07956
95.4%
1354
 
4.2%
232
 
0.4%
31
 
< 0.1%

webinar_total_12
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
8343 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
08343
100.0%

Length

2021-06-01T16:57:13.495631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:13.569612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
08343
100.0%

Most occurring characters

ValueCountFrequency (%)
08343
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08343
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08343
100.0%

is_cardiologist
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0
7340 
1
1003 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
07340
88.0%
11003
 
12.0%

Length

2021-06-01T16:57:13.769256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:13.836539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
07340
88.0%
11003
 
12.0%

Most occurring characters

ValueCountFrequency (%)
07340
88.0%
11003
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07340
88.0%
11003
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07340
88.0%
11003
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07340
88.0%
11003
 
12.0%

is_gp
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
1
7340 
0
1003 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8343
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
17340
88.0%
01003
 
12.0%

Length

2021-06-01T16:57:14.004831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:14.073200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
17340
88.0%
01003
 
12.0%

Most occurring characters

ValueCountFrequency (%)
17340
88.0%
01003
 
12.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8343
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
17340
88.0%
01003
 
12.0%

Most occurring scripts

ValueCountFrequency (%)
Common8343
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
17340
88.0%
01003
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII8343
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17340
88.0%
01003
 
12.0%

years_since_graduation
Real number (ℝ≥0)

Distinct60
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.08845739
Minimum3
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:14.547949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q117
median28
Q337
95-th percentile46
Maximum68
Range65
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.37045419
Coefficient of variation (CV)0.4566688319
Kurtosis-0.9050269057
Mean27.08845739
Median Absolute Deviation (MAD)10
Skewness0.00792055519
Sum225999
Variance153.028137
MonotonicityNot monotonic
2021-06-01T16:57:14.676075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32247
 
3.0%
31246
 
2.9%
33245
 
2.9%
35244
 
2.9%
36238
 
2.9%
30236
 
2.8%
38228
 
2.7%
26221
 
2.6%
39220
 
2.6%
11218
 
2.6%
Other values (50)6000
71.9%
ValueCountFrequency (%)
31
 
< 0.1%
442
 
0.5%
586
 
1.0%
6134
1.6%
7151
1.8%
8199
2.4%
9202
2.4%
10185
2.2%
11218
2.6%
12177
2.1%
ValueCountFrequency (%)
681
 
< 0.1%
632
 
< 0.1%
602
 
< 0.1%
595
 
0.1%
585
 
0.1%
574
 
< 0.1%
5610
0.1%
5516
0.2%
5420
0.2%
5324
0.3%

gender_female
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
1.0
5468 
0.0
2875 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25029
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05468
65.5%
0.02875
34.5%

Length

2021-06-01T16:57:14.900653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:14.965926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.05468
65.5%
0.02875
34.5%

Most occurring characters

ValueCountFrequency (%)
011218
44.8%
.8343
33.3%
15468
21.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16686
66.7%
Other Punctuation8343
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011218
67.2%
15468
32.8%
Other Punctuation
ValueCountFrequency (%)
.8343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011218
44.8%
.8343
33.3%
15468
21.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII25029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011218
44.8%
.8343
33.3%
15468
21.8%

gender_male
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0.0
5468 
1.0
2875 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25029
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05468
65.5%
1.02875
34.5%

Length

2021-06-01T16:57:15.128585image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:15.193024image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.05468
65.5%
1.02875
34.5%

Most occurring characters

ValueCountFrequency (%)
013811
55.2%
.8343
33.3%
12875
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16686
66.7%
Other Punctuation8343
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
013811
82.8%
12875
 
17.2%
Other Punctuation
ValueCountFrequency (%)
.8343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
013811
55.2%
.8343
33.3%
12875
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII25029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
013811
55.2%
.8343
33.3%
12875
 
11.5%

hospital
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
0.0
7415 
1.0
928 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25029
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.07415
88.9%
1.0928
 
11.1%

Length

2021-06-01T16:57:15.366883image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:15.437033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.07415
88.9%
1.0928
 
11.1%

Most occurring characters

ValueCountFrequency (%)
015758
63.0%
.8343
33.3%
1928
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16686
66.7%
Other Punctuation8343
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
015758
94.4%
1928
 
5.6%
Other Punctuation
ValueCountFrequency (%)
.8343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
015758
63.0%
.8343
33.3%
1928
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII25029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
015758
63.0%
.8343
33.3%
1928
 
3.7%

office
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
1.0
7415 
0.0
928 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25029
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.07415
88.9%
0.0928
 
11.1%

Length

2021-06-01T16:57:15.618692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-06-01T16:57:15.732825image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.07415
88.9%
0.0928
 
11.1%

Most occurring characters

ValueCountFrequency (%)
09271
37.0%
.8343
33.3%
17415
29.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number16686
66.7%
Other Punctuation8343
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09271
55.6%
17415
44.4%
Other Punctuation
ValueCountFrequency (%)
.8343
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09271
37.0%
.8343
33.3%
17415
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII25029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09271
37.0%
.8343
33.3%
17415
29.6%

prescription_total
Real number (ℝ≥0)

ZEROS

Distinct66
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.243078029
Minimum0
Maximum131
Zeros1527
Zeros (%)18.3%
Negative0
Negative (%)0.0%
Memory size65.3 KiB
2021-06-01T16:57:15.818054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q35
95-th percentile14
Maximum131
Range131
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.438680739
Coefficient of variation (CV)1.517455181
Kurtosis56.14739167
Mean4.243078029
Median Absolute Deviation (MAD)2
Skewness5.599981137
Sum35400
Variance41.45660966
MonotonicityNot monotonic
2021-06-01T16:57:15.948570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21566
18.8%
01527
18.3%
11201
14.4%
3907
10.9%
4770
9.2%
5444
 
5.3%
6412
 
4.9%
7291
 
3.5%
8216
 
2.6%
10157
 
1.9%
Other values (56)852
10.2%
ValueCountFrequency (%)
01527
18.3%
11201
14.4%
21566
18.8%
3907
10.9%
4770
9.2%
5444
 
5.3%
6412
 
4.9%
7291
 
3.5%
8216
 
2.6%
9154
 
1.8%
ValueCountFrequency (%)
1311
< 0.1%
1001
< 0.1%
961
< 0.1%
831
< 0.1%
821
< 0.1%
781
< 0.1%
741
< 0.1%
731
< 0.1%
721
< 0.1%
711
< 0.1%

Interactions

2021-06-01T16:56:55.063759image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.194167image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.289481image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.381098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.474796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.568020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.657640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.754268image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.849813image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:55.943465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.033532image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.129145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.223117image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.317906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.664215image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.763903image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.854578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:56.945239image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.033451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.127640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.225755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.317126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.414078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.518916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.614261image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.706089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.797792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.886806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:57.978087image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.068438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.153868image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.246665image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.338527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.427391image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.513342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.608320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.699432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.793863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.888299image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:58.980964image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.075066image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.167300image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.256104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.351821image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.446133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.538676image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.629921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.726346image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.820366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:56:59.912607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.004357image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.095453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.187413image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.277991image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.364403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.457249image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.551554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.644122image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.731730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.825747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:00.916932image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.003680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.092274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.442689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.541865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.642941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.721638image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.808733image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.895195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:01.979424image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.064939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.153547image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.239726image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.335892image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.431491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.525934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.623264image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.718245image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.809134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:02.905505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.001682image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.095635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.186104image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.282984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.377354image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.473258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.568925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.663737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.759959image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.854596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:03.944855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.042089image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.138664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.232985image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.323291image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.420216image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.514926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.608518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.702235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.793625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.886427image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:04.977522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.065897image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.159919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.254243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.344679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.431687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.525156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.617656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.705693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.793226image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.878854image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:05.966195image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.052769image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.134030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.222687image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.310571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.395713image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.477501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.566811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.653698image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.751966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.849197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:06.944061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.354550image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.455939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.543459image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.641672image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.739477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.833969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:07.924549image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.021871image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.118201image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.212849image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.306683image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.398306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.491824image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.584565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.673379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.768537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.863002image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:08.954318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:09.042958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-06-01T16:57:09.138096image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-06-01T16:57:16.120497image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-06-01T16:57:16.400850image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-06-01T16:57:16.694940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-06-01T16:57:16.976053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-06-01T16:57:17.198083image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-06-01T16:57:09.357554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-06-01T16:57:09.862286image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

rep_idconference_total_03conference_total_06conference_total_09conference_total_12email_open_total_03email_open_total_06email_open_total_09email_open_total_12f2f_total_03f2f_total_06f2f_total_09f2f_total_12webinar_total_03webinar_total_06webinar_total_09webinar_total_12is_cardiologistis_gpyears_since_graduationgender_femalegender_malehospitalofficeprescription_total
0100001010014690021001231.00.00.01.03
1101001000210200000001270.01.00.01.02
2101000001101340001001290.01.01.00.083
3102000000100000000001221.00.01.00.02
4103000102123670000001270.01.00.01.04
5100000010223420100001441.00.00.01.05
6101000010103220000001351.00.00.01.04
7101100021202400000001421.00.00.01.02
810410000012913100000010451.00.00.01.08
9101000000224660000001190.01.00.01.00

Last rows

rep_idconference_total_03conference_total_06conference_total_09conference_total_12email_open_total_03email_open_total_06email_open_total_09email_open_total_12f2f_total_03f2f_total_06f2f_total_09f2f_total_12webinar_total_03webinar_total_06webinar_total_09webinar_total_12is_cardiologistis_gpyears_since_graduationgender_femalegender_malehospitalofficeprescription_total
8333116110000001210000001210.01.00.01.09
833420700002121010001000190.01.00.01.05
8335158010012000100000010201.00.00.01.022
833618400002113001000000181.00.00.01.05
833715200000200010000000171.00.00.01.06
8338160000001200140000010111.00.01.00.021
833912300001000011000000141.00.00.01.01
8340116001001020010000001161.00.00.01.03
8341152000011120010000001271.00.00.01.09
834211500000300001000000131.00.00.01.01